Semantics: Representations and Analyses

1. Semantics: Representations and Analyses CS 4705

2. What kinds of meaning do we want to capture? • Categories/entities • IBM, Jane, a black cat, Pres. Bush • Events • running a mile, AS elected governor of CA • Time • Oct 30, next week, in 2 years • Aspect • Jack knows how to run. Jack is running. Jack ran the mile in 5 min. • Beliefs, Desires and Intentions (BDI)

3. What Can Serve as a Meaning Representation? • Anything that allows us to • Answer questions (What is the tallest building in the world?) • Determine truth (Is the blue block on the red block?) • Draw inferences (If the blue block is on the red block and the red block is on the tallest building in the world, then the blue block is on the tallest building in the world)

4. Meaning Representations • All represent ‘linguistic meaning’ of I have a car and state of affairs in some world • All consist of structures, composed of symbols representing objects and relations among them • FOPC: • Semantic Net: having haver had-thing speaker car

5. Conceptual Dependency Diagram: Car  Poss-By Speaker • Frame Having Haver: S HadThing: Car

6. A Standard Representation: Predicate-Argument Structure • Represents concepts and relationships among them • Nouns as concepts or arguments (red(ball)) • Adjectives, adverbs, verbs as predicates (red(ball)) • Subcategorization (or, argument) frames specify number, position, and syntactic category of arguments • NP likes NP • NP likes Inf-VP • NP likes NP Inf-VP

7. Semantic (Thematic) Roles • Subcat frames link arguments in surface structure with their semantic roles • Agent: George hit Bill. Bill was hit by George. • Patient: George hit Bill. Bill was hit by George. • Selectional Restrictions: constraints on the types ofarguments verbs take George assassinated the senator. *The spider assassinated the fly. assassinate: intentional (political?) killing

8. First Order Predicate Calculus • Not ideal as a meaning representation and doesn't do everything we want -- but better than many… • Supports the determination of truth • Supports compositionality of meaning • Supports question-answering (via variables) • Supports inference

9. NL Mapping to FOPC • Terms: constants, functions, variables • Constants: objects in the world, e.g. Huey • Functions: concepts, e.g. sisterof(Huey) • Variables: x, e.g. sisterof(x) • Predicates: symbols that refer to relations that hold among objects in some domain or properties that hold of some object in a domain likes(Huey, kibble) cat(Huey)

10. Logical connectives permit compositionality of meaning kibble(x)  likes(Huey,x) “Huey likes kibble” cat(Vera) ^ odd(Vera) “Vera is an odd cat” sleeping(Huey) v eating(Huey) “Huey either is sleeping or eating” • Sentences in FOPC can be assigned truth values • Atomic formulae are T or F based on their presence or absence in a DB (Closed World Assumption?) • Composed meanings are inferred from DB and meaning of logical connectives • cat(Huey) • sibling(Huey,Vera) • cat(Huey) ^ sibling(Huey,Vera)  cat(Vera)

11. Limitations: • Do ‘and’ and ‘or’ in natural language really mean ‘^’ and ‘v’? Mary got married and had a baby. And then… Your money or your life! • Does ‘’ mean ‘if’? If you go, I’ll meet you there. • How do we represent other connectives? She was happy but ignorant. • Non-compositionality “Fake gun” “small building” v. “small child” idiom, metaphor, metonymy • Synonymy “The stock market surged at the start of the day” v. “The stock market opened high”

12. Quantifiers: • Existential quantification: There is a unicorn in my garden. Some unicorn is in my garden. • Universal quantification: The unicorn is a mythical beast. Unicorns are mythical beasts. • Many? A few? Several? A couple?

13. Temporal Representations • How do we represent time and temporal relationships between events? Last year Martha Stewart was happy but soon she will be in prison. • Where do we get temporal information? • Verb tense • Temporal expressions • Sequence of presentation • Linear representations: Reichenbach ‘47

14. Utterance time (U): when the utterance occurs • Reference time (R): the temporal point-of-view of the utterance • Event time (E): when events described in the utterance occur George is eating a sandwich. -- E,R,U  George had eaten a sandwich (when he realized…) E – R – U  George will eat a sandwich. --U,R – E  While George was eating a sandwich his mother arrived. --E,R – U 

15. Verbs and Event Types: Aspect • Statives: states or properties of objects at a particular point in time I am hungry. • Activities: events with no clear endpoint I am eating. • Accomplishments: events with durations and endpoints that result in some change of state I ate dinner. • Achievements: events that change state but have no particular duration – they occur in an instant I got the bill.

16. Beliefs, Desires and Intentions • Very hard to represent internal speaker states like believing, knowing, wanting, assuming, imagining • Not well modeled by a simple DB lookup approach so.. • Truth in the world vs. truth in some possible world George imagined that he could dance. George believed that he could dance. • Augment FOPC with special modal operators that take logical formulae as arguments, e.g. believe,know

17. Believes(George, dance(George)) Knows(Bill,Believes(George,dance(George))) • Mutual belief: I believe you believe I believe…. • Practical importance: modeling belief in dialogue • Clark’s grounding

18. Summary • Many hard problems in full semantic representation: • Temporal relations: tense, aspect • BDI • Current representations impoverished in many respects • Next time: Read Ch 15:1-4